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While valuable AI projects are just starting to emerge, 46% of CIOs have plans to implement the technology in the future. — Gartner, 2018

Executives have begun piloting AI projects by buying, building, and outsourcing the technology, and lessons for future business implementations are emerging. — Gartner, 2018

While the value of artificial intelligence (AI) is just beginning to emerge in the enterprise, some 46% of CIOs have plans to implement the technology in the future, according to a survey from research firm Gartner.

"Despite huge levels of interest in AI technologies, current implementations remain at quite low levels," Gartner analyst Whit Andrews wrote in a press release. "However, there is potential for strong growth as CIOs begin piloting AI programmes through a combination of buy, build and outsource efforts."

Early AI projects have demonstrated the value of the technology, but have also showed some of the challenges linked to its adoption, the release said. Here are four of the main lessons that the enterprise has learned from early AI initiatives.

1. Aim low at first

One of the biggest mistakes a company can make is to aim too high with its goals for its AI projects. In the release, Andrews urged company leaders not to seek outcomes such as financial gains at the start. Rather, he wrote, shoot for softer outcomes like "process improvements, customer satisfaction or financial benchmarking."

Companies will likely generate only lessons from their AI projects, or ideas for other initiatives, at first. If a financial target is required to start the project, make it as low as possible, Andrews wrote.

2. Focus on augmenting people, not replacing them

One of the biggest fears of frontline workers is also one of the biggest benefits to a company's bottom line: Reduced headcount. However, Andrews wrote that executives should focus on using AI to improve the work done by their employees, not supplant it.

"We advise our clients that the most transformational benefits of AI in the near term will arise from using it to enable employees to pursue higher-value activities," Andrews wrote.

3. Plan for knowledge transfer

Effectively using AI requires a company to know about the data that powers the AI program. Because many firms lack such skills internally, the release said, they plan to outsource the required data science work to external providers. However, "relying mostly on external suppliers for these skills is not an ideal long-term solution," Jim Hare, research vice president at Gartner, wrote in the release. "Therefore, ensure that early AI projects help transfer knowledge from external experts to your employees, and build up your organization's in-house capabilities before moving on to large-scale projects."

4. Choose transparent AI solutions

Regardless of how skilled an organization is, it will likely need some help from external software or systems when it comes to AI implementation. Because of this, leaders should seek to understand how those external tools make their decisions and use data. Furthermore, transparency might even be a legal requirement in auditing and regulatory situations, the release said.

"Whether an AI system produces the right answer is not the only concern," Andrews wrote. "Executives need to understand why it is effective, and offer insights into its reasoning when it's not."

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